Papers by Tao Feng
CE-RM: A Pointwise Generative Reward Model Optimized via Two-Stage Rollout and Unified Criteria (2026.findings-acl)
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| Challenge: | Existing studies have shown that rule-based evaluation methods are ineffective for open-ended natural language generation. |
| Approach: | They propose a pointwise generative reward model with a dedicated two-stage rollout method and unified query-based criteria that can be trained with 5.7K high-quality data. |
| Outcome: | The proposed model achieves superior performance on diverse reward model benchmarks, especially in Best-of-N scenarios, and delivers more effective improvements in downstream RL practice. |
DisCo_Speech: Controllable Zero-Shot Speech Generation with A Disentangled Speech Codec (2026.acl-long)
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Tao Li, Wenshuo Ge, Zhichao Wang, Zihao Cui, Yong Ma, Yingying Gao, Chao Deng, Shilei Zhang, Junlan Feng
| Challenge: | DisCo-Speech is a zero-shot controllable text-to-speech framework . standard codecs entangle timbre and prosody, which hinders independent control in continuation-based LMs. |
| Approach: | They propose a disentangled speech codec and an LM-based generator to solve this problem . they propose fusion and reconstruction that merges content and prosody into unified tokens . |
| Outcome: | DisCo-Speech achieves competitive voice cloning and superior zero-shot prosody control. |
CateEA: Enhancing Entity Alignment via Implicit Category Supervision (2025.coling-main)
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| Challenge: | Existing Entity Alignment methods neglect the inherent semantic information of entities, limiting alignment precision and robustness. |
| Approach: | They propose to combine implicit category information into multi-modal representations by generating pseudo-category labels from entity embeddings and integrating them into a multi-task learning framework. |
| Outcome: | Experiments on benchmark datasets show that CateEA outperforms state-of-the-art methods in various settings. |
Generative-to-Discriminative Test-Time Adaptation via Manifold-Aware Diffusion and Bayesian Distillation (2026.findings-acl)
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Boyun Zhang, Zequn Xie, Fangming Feng, Zihan Zhang, Yongbo He, Chuxin Wang, Sihang Cai, Tao Jin, Qifei Zhang
| Challenge: | Existing discriminative approaches suffer from "confident but wrong" failure mode, blindly adapting to OOD noise leading to error accumulation. |
| Approach: | They propose a TTA framework that harmonizes the robustness of generative diffusion models with the efficiency of discriminative regression networks via Bayesian Diffusion Distillation (BDD). |
| Outcome: | The proposed framework reduces MAE from 0.6872 to 0.5673 and boosts binary accuracy by 5.81 percentage points (reaching 57.33%) it also reduces the MAE of the MOSI to SIMS shift and achieves an 11.18-point gain over the baseline. |
A Pre-training Strategy for Zero-Resource Response Selection in Knowledge-Grounded Conversations (2021.acl-long)
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| Challenge: | Existing methods to train retrieval-based dialogue systems rely on crowd-sourced data . however, it is difficult to collect large-scale dialogues that are grounded on background knowledge . |
| Approach: | They propose to decompose training of knowledge-grounded response selection into three tasks . they propose to combine query-passage matching task with query-dialogue history matching task . |
| Outcome: | Experimental results show that the proposed model can perform comparable to existing methods . the retrieval-based system can leverage background knowledge when conversing with humans . |
Chain-of-Discussion: A Multi-Model Framework for Complex Evidence-Based Question Answering (2025.coling-main)
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| Challenge: | Large Language Models (LLMs) have demonstrated remarkable language generation capabilities, propelling advancements in various understanding/generation tasks, including opendomain question answering (QA). |
| Approach: | They propose a chain-of- Discussion framework to leverage synergy among multiple open-source Large Language Models (LLMs) aiming to provide more correct and more comprehensive answers for open-ended QA, although they are not strong enough individually. |
| Outcome: | The proposed framework leverages the synergy among multiple open-source Large Language Models (LLMs) to provide more correct and comprehensive answers for open-ended QA, although they are not strong enough individually. |
TexSmart: A System for Enhanced Natural Language Understanding (2021.acl-demo)
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Lemao Liu, Haisong Zhang, Haiyun Jiang, Yangming Li, Enbo Zhao, Kun Xu, Linfeng Song, Suncong Zheng, Botong Zhou, Dick Zhu, Xiao Feng, Tao Chen, Tao Yang, Dong Yu, Feng Zhang, ZhanHui Kang, Shuming Shi
| Challenge: | TexSmart supports fine-grained named entity recognition (NER) Large-scale fine-granular entity types are expected to provide richer semantic information for downstream NLP applications. |
| Approach: | They introduce TexSmart, a text understanding system that supports fine-grained named entity recognition (NER) and enhanced semantic analysis functionalities. |
| Outcome: | The proposed system supports fine-grained named entity recognition (NER) and enhanced semantic analysis functions. |
PLOME: Pre-training with Misspelled Knowledge for Chinese Spelling Correction (2021.acl-long)
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| Challenge: | Chinese spelling correction (CSC) is a task to detect and correct spelling errors in texts. |
| Approach: | They propose a Pre-trained masked Language model with Misspelled knowledgE (PLOME) which jointly learns how to understand language and correct spelling errors. |
| Outcome: | The proposed model outperforms state-of-the-art methods on widely used benchmarks and achieves superior performance against existing models. |
Rethinking Task-Specific Knowledge Distillation: Contextualized Corpus as Better Textbook (2022.emnlp-main)
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| Challenge: | Existing methods for knowledge distillation use a two-stage paradigm: general distillation with a task-agnostic general corpus and task-specific distillation using augmented task- specific corpus. |
| Approach: | They propose a contextualized corpus that contextualizes task corpus with large-scale general corpus through relevance-based text retrieval to improve student learning. |
| Outcome: | The proposed model improves on the GLUE benchmark and shows that it is better than generalized corpus and augmented task-specific corpus. |
Probing Multimodal Large Language Models for Global and Local Semantic Representations (2024.lrec-main)
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| Challenge: | Existing studies have focused on the ability of MLLMs to generate single tokens one by one, while lacking studies about how their representation vectors can encode global multimodal information. |
| Approach: | They propose to use image-caption corpus to train Multimodal Large Language Models (MLLMs) . they find that the topmost layers encode more global semantic information . |
| Outcome: | The proposed models can encode more global semantic information, rather than the topmost layers, and perform better on visual-language entailment tasks. |
GAPO: Learning Preferential Prompt through Generative Adversarial Policy Optimization (2025.acl-long)
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Zhouhong Gu, Xingzhou Chen, Xiaoran Shi, Tao Wang, Suhang Zheng, Tianyu Li, Hongwei Feng, Yanghua Xiao
| Challenge: | Existing methods for achieving this require a limited understanding of constraints and can be hallucinating or brittle. |
| Approach: | They propose a framework that combines adversarial training dynamics with an encoder-only reward model to progressively learn and adapt to increasingly complex constraints. |
| Outcome: | Extensive experiments show that GAPO significantly outperforms existing methods like PPO, DPO, and KTO in fine-grained constraints. |
EpiCoDe: Boosting Model Performance Beyond Training with Extrapolation and Contrastive Decoding (2025.findings-acl)
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| Challenge: | Existing methods to enhance performance of Large language models are limited due to the cost of training data and privacy concerns. |
| Approach: | They propose a method that enhances a finetuned model with its inferior version and adopts contrastive decoding to reduce predicted errors. |
| Outcome: | The proposed method outperforms existing methods in data-scarcity scenarios across three domains and shows that it is more robust and robust. |
Unlocking the Potential of Model Merging for Low-Resource Languages (2024.findings-emnlp)
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| Challenge: | Adapting large language models (LLMs) to new languages requires continual pre-training followed by supervised fine-tuning. |
| Approach: | They propose a model merging solution that integrates LLMs with distinct capabilities into a single model without additional training. |
| Outcome: | The proposed model merging outperforms CT-then-SFT in low-resource languages with scarce data. |
Two-Stage Regularization-Based Structured Pruning for LLMs (2026.acl-long)
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Mingkuan Feng, Jinyang Wu, Siyuan Liu, Shuai Zhang, Hongjian Fang, Ruihan Jin, Feihu Che, Pengpeng Shao, Zhengqi Wen, Jianhua Tao
| Challenge: | Structural pruning is a promising solution for large language models . prior structured pruning methods remove unimportant parameters based on certain metrics . |
| Approach: | They propose a structural pruning method that iteratively learns the weights of transformer layers by adding their l1-norm to the loss function. |
| Outcome: | The proposed pruning method outperforms strong layer-wise pruning methods without requiring retraining. |
Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation (2025.findings-emnlp)
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| Challenge: | Large Language Models struggle to adapt content to users with differing cognitive capacities, leading to cognitive misalignment. |
| Approach: | They propose a cognitive-level alignment framework that aligns both knowledge complexity and presentation style with user cognition. |
| Outcome: | The proposed framework aligns knowledge complexity and presentation style with user cognition. |
On the Reliability of Large Language Models for Causal Discovery (2025.acl-long)
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| Challenge: | Existing statistical methods to identify causal relationships from observational data remain elusive. |
| Approach: | They examine the impact of memorization for accurate causal relation prediction, the influence of incorrect causal relations in pre-training data and the contextual nuances that influence LLMs’ understanding of causal relations. |
| Outcome: | The proposed models are effective in recognizing causal relations that occur frequently in pre-training data, but their ability to generalize to new or rare causal relations is limited. |
Learning a Matching Model with Co-teaching for Multi-turn Response Selection in Retrieval-based Dialogue Systems (P19-1)
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| Challenge: | Existing methods for learning a robust matching model from noisy training data are retrieval-based or generation-based. |
| Approach: | They propose a general co-teaching framework that learns matching models from noisy training data. |
| Outcome: | The proposed learning framework can improve existing models on two public data sets. |
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation (2023.acl-long)
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| Challenge: | MMDialog is a dataset of 1.08 million real-world dialogues with 1.53 million unique images across 4,184 topics. |
| Approach: | They propose to use a curated set of 1.08 million dialogues with 1.53 million unique images to generalize the open domain. |
| Outcome: | The proposed system can predict responses to multi-modal content with state-of-the-art techniques and measure their performance. |
Less is More: Improving LLM Reasoning with Minimal Test-Time Intervention (2026.acl-long)
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| Challenge: | Recent advances in large language models (LLMs) have focused on test-time scaling to improve reasoning quality but at the cost of efficiency. |
| Approach: | They propose a training-free framework that enhances reasoning accuracy and stability with minimal overhead. |
| Outcome: | The proposed framework yields consistent gains across general, coding, and STEM tasks while remaining highly efficient. |
PACE: Prefix-Protected and Difficulty-Aware Compression for Efficient Reasoning (2026.findings-acl)
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Ruixiang Feng, Yuntao Wen, Silin Zhou, Ke Shi, Yifan Wang, Ran Le, Zhenwei An, Zongchao Chen, Chen Yang, Guangyue Peng, Yiming Jia, Dongsheng Wang, Tao Zhang, Lisi Chen, Yang Song, Shen Gao, Shuo Shang
| Challenge: | Existing LRMs often suffer from "overthinking" and excessively long reasoning traces . a dual-level framework for length compression of LRM is proposed . |
| Approach: | They propose a framework for prefix-protected and difficulty-aware compression under hierarchical supervision. |
| Outcome: | The proposed framework reduces token usage while improving accuracy on math benchmarks. |
CausalScore: An Automatic Reference-Free Metric for Assessing Response Relevance in Open-Domain Dialogue Systems (2025.coling-main)
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| Challenge: | Existing metrics for dialogue quality evaluation show low correlation with human judgements . current metrics do not accurately evaluate dialogue responses based on dialogue history . |
| Approach: | They propose a new metric measuring causal strength between dialogue histories and responses . they collect a dialogue dataset with human-annotated causal relations and pairwise human judgements . |
| Outcome: | The proposed metric outperforms existing state-of-the-art metrics in human judgements . it is based on a dialogue dataset with human-annotated causal relations and human judgement sets . |
VideoPro: Adaptive Program Reasoning for Long Video Understanding (2026.acl-long)
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Chenglin Li, Feng Han, Yikun Wang, Ruilin Li, Shuai Dong, Haowen Hou, Haitao Li, Qianglong Chen, Feng Tao, Jingqi Tong, Yin Zhang, Jiaqi Wang
| Challenge: | Existing methods for understanding long videos are limited due to the sparsity of visual evidence relevant to a given query. |
| Approach: | They propose a framework that enables VideoLLMs to reason over long videos and refine their predictions through executable programs. |
| Outcome: | The proposed framework outperforms existing methods across long-video understanding benchmarks. |
Reciprocal Learning of Knowledge Retriever and Response Ranker for Knowledge-Grounded Conversations (2022.coling-1)
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| Challenge: | Recent work on grounding dialogue agents with knowledge documents has sparked increased attention . hand-labeling data to that end is time-consuming and many datasets lack knowledge annotations . |
| Approach: | They propose a reciprocal learning approach to optimize a knowledge retriever and a response ranker for knowledge-grounded response retrieval without ground-truth knowledge labels. |
| Outcome: | The proposed model outperforms previous state-of-the-art methods on two public benchmarks. |
Synergistic Interplay between Search and Large Language Models for Information Retrieval (2024.acl-long)
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| Challenge: | Information retrieval (IR) is an indispensable technique for locating relevant resources from vast amounts of data. |
| Approach: | They propose a framework that facilitates information refinement through synergy between RMs and LLMs. |
| Outcome: | The proposed framework improves the performance of large-scale retrieval benchmarks on web searches and low-resource retrieval tasks. |
MimicLM: Zero-Shot Voice Imitation through Autoregressive Modeling of Pseudo-Parallel Speech Corpora (2026.findings-acl)
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Tao Feng, Yuxiang Wang, Yuancheng Wang, Xueyao Zhang, Dekun Chen, Chaoren Wang, Xun Guan, Zhizheng Wu
| Challenge: | Existing approaches to voice imitation use complex model design and a quality ceiling when synthetic speech is used as training *sources*. |
| Approach: | They propose a model that uses synthetic speech as training *sources* while retaining real recordings as *targets*. |
| Outcome: | The proposed model outperforms existing methods in naturalness while maintaining competitive similarity scores across speaker identity, accent, and emotion dimensions. |
Type Enhanced BERT for Correcting NER Errors (2023.findings-acl)
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| Challenge: | Named entity recognition (NER) is the task of identifying spans that belong to particular categories, such as person, location, organization, etc. |
| Approach: | They propose a method that integrates named entity’s type information into BERT by an adapter layer and integrates it into a gazetteer. |
| Outcome: | The proposed method outperforms baselines in multiple corpus. |
MC2: Towards Transparent and Culturally-Aware NLP for Minority Languages in China (2024.acl-long)
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| Challenge: | MC2 is the largest open-source corpus of minority languages in china . MC2, however, includes four underrepresented languages: Tibetan, Uyghur, Kazakh, and Mongolian . |
| Approach: | They propose a multilingual corpus of minority languages in China that includes four underrepresented languages . they prioritize accuracy while enhancing diversity by using a quality-centric approach . |
| Outcome: | The proposed model prioritizes accuracy while enhancing diversity, the authors say . MC2 includes four underrepresented languages: Tibetan, Uyghur, Kazakh, and Mongolian . |
Learning to Organize a Bag of Words into Sentences with Neural Networks: An Empirical Study (2021.naacl-main)
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| Challenge: | Existing approaches to encode natural languages without orders are lacking. |
| Approach: | They conduct a comprehensive analysis of the ability of neural models to organize sentences from a bag of words under three typical scenarios. |
| Outcome: | The proposed models can reorder or reconstruct sentences from a bag of words under three typical scenarios. |
KLMo: Knowledge Graph Enhanced Pretrained Language Model with Fine-Grained Relationships (2021.findings-emnlp)
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| Challenge: | Existing knowledge-enhanced pretrained language models focus on entity information and ignore fine-grained relationships between entities. |
| Approach: | They propose to incorporate KG into the language learning process to obtain a KG-enhanced pretrained Language Model. |
| Outcome: | The proposed model improves on several knowledge-driven tasks, such as entity typing and relation classification, compared with the state-of-the-art knowledge-enhanced PLMs. |
Sampling Matters! An Empirical Study of Negative Sampling Strategies for Learning of Matching Models in Retrieval-based Dialogue Systems (D19-1)
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| Challenge: | Existing studies focus on constructing a matching model with sophisticated neural architectures, but do little to how to effectively learn such architectures from data. |
| Approach: | They propose to sample negative examples to automatically construct a training set for effective model learning in retrieval-based dialogue systems by using four sampling strategies. |
| Outcome: | The proposed learning method improves the performance of matching models on two benchmarks with three matching models. |
Multi-Granularity Structural Knowledge Distillation for Language Model Compression (2022.acl-long)
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| Challenge: | Existing methods to transfer knowledge to a small model are not enough to represent the rich semantics of a text. |
| Approach: | They propose to distill the knowledge to a student hierarchically across layers using a large teacher-student framework. |
| Outcome: | Experimental results show that the proposed method outperforms distillation methods on GLUE benchmark. |
Arxiv Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance (2024.emnlp-demo)
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| Challenge: | Existing solutions for document QA fail to provide personalized and up-to-date information efficiently. |
| Approach: | They propose to deploy a self-evolving, efficient LLM system that can offer personalized research services, maintaining a real-time updated database. |
| Outcome: | The proposed system saves 69.92% of time after efficient deployment. |
Teaching Small Language Models Reasoning through Counterfactual Distillation (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) have demonstrated remarkable performance in a wide range of downstream tasks. |
| Approach: | They propose a counterfactual distillation framework that leverages LLMs to generate high-quality counterfacts and utilizes multi-view CoT to enhance the diversity of reasoning samples. |
| Outcome: | The proposed framework enhances reasoning capabilities of large language models and is more robust to OOD data. |
CORE: Cooperative Training of Retriever-Reranker for Effective Dialogue Response Selection (2023.acl-long)
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| Challenge: | Existing methods to train retrieval-based dialogue systems are suboptimal . existing methods to optimize retrieval and rerank modules are sub-optimal, causing sub-optimum performance. |
| Approach: | They propose a retrieval-based dialogue system with a fast retriever and a smart response reranker that combine the best of both worlds. |
| Outcome: | The proposed method can learn from each other and evolve together . it can be used in industrial applications and has powered industrial applications. |
Structured Attention for Unsupervised Dialogue Structure Induction (2020.emnlp-main)
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| Challenge: | Using structured attention, a model can learn dialogue structure in unsupervised fashion. |
| Approach: | They propose to incorporate structured attention layers into a Variational Recurrent Neural Network model with discrete latent states to learn dialogue structure in an unsupervised fashion. |
| Outcome: | The proposed model learns semantic structures similar to templates used to generate a dialogue corpus on two-party datasets and on multi-party dialogues, disentangling dialogues without human annotation. |
RadialRouter: Structured Representation for Efficient and Robust Large Language Models Routing (2025.findings-emnlp)
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| Challenge: | Current routing methods are limited in exploring the connection between query and LLM characteristics. |
| Approach: | They propose a framework for LLM routing that uses a transformer-based backbone and a radial structure to articulate the query-LLMs relationship. |
| Outcome: | The proposed framework outperforms existing routing methods by 9.2% and 5.8% on RouterBench. |
EmoCharacter: Evaluating the Emotional Fidelity of Role-Playing Agents in Dialogues (2025.naacl-long)
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| Challenge: | EmoCharacter evaluates emotional fidelity of role-playing agents in dialogues . current evaluations focus on personality fidelity, tone imitation, and knowledge consistency . |
| Approach: | They propose a benchmark to assess emotional fidelity of role-playing agents in dialogues using large language models. |
| Outcome: | The proposed benchmark measures emotional fidelity of role-playing agents and the characters they portray. |
Harder Task Needs More Experts: Dynamic Routing in MoE Models (2024.acl-long)
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Quzhe Huang, Zhenwei An, Nan Zhuang, Mingxu Tao, Chen Zhang, Yang Jin, Kun Xu, Kun Xu, Liwei Chen, Songfang Huang, Yansong Feng
| Challenge: | Unlike existing MoE approaches that rely on fixed TopK Routing, our dynamic expert selection framework dynamically allocates experts based on the confidence level in expert selection for each input. |
| Approach: | They propose a dynamic expert selection framework that dynamically allocates experts based on the confidence level in expert selection for each input. |
| Outcome: | The proposed method achieves an average improvement of 0.7% with less than 90% activated parameters and outperforms dense models in QA and machine translation tasks. |
Length-Adaptive Distillation: Customizing Small Language Model for Dynamic Token Pruning (2023.findings-emnlp)
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| Challenge: | Existing methods to accelerate inference speed are model compression and dynamic computation (e.g., dynamic token pruning). |
| Approach: | They propose a two-stage knowledge distillation framework that produces a customized small language model for dynamic token pruning. |
| Outcome: | The proposed framework can make the small language model more customized for dynamic token pruning and achieve better speed-performance trade-off. |
How to Represent Context Better? An Empirical Study on Context Modeling for Multi-turn Response Selection (2022.findings-emnlp)
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| Challenge: | Existing work on building a conversational system for open domain human-machine conversation is attracting more attention . early models concatenate all utterances or independently encode each dialogue turn, which may lead to an inadequate understanding of dialogue status. |
| Approach: | They propose to use a turn-aware context modeling layer to adapt existing models . they propose to model multi-turn contexts from the perspective of sequential relationship, local relationship, and query-alike manner . |
| Outcome: | The proposed method can be adapted to several advanced response selection models. |
TWEET-FID: An Annotated Dataset for Multiple Foodborne Illness Detection Tasks (2022.lrec-1)
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| Challenge: | Approximately 1 in 6 Americans (or 48 million people) are sickened by foodborne illness each year. |
| Approach: | They propose to use Twitter's TWEET-FID dataset to create annotated datasets for multiple foodborne illness incident detection tasks. |
| Outcome: | The proposed dataset is the first publicly available annotated dataset for multiple foodborne illness incident detection tasks. |
TemplateRL: Structured Template-Guided Reinforcement Learning for LLM Reasoning (2026.findings-acl)
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Jinyang Wu, Chonghua Liao, Mingkuan Feng, Shuai Zhang, Zhengqi Wen, Haoran Luo, Ling Yang, Huazhe Xu, Jianhua Tao
| Challenge: | Existing RL methods rely on unstructured self-sampling to fit scalar rewards, resulting in inefficient rollouts. |
| Approach: | They propose a structured template-guided RL framework that augments policy optimization with explicit template guidance. |
| Outcome: | Experiments show that TemplateRL outperforms GRPO and GRPI by 99% on AIME and 41% on AMC with superior stability on weak models and remarkable cross-domain generalization. |
MiLiC-Eval: Benchmarking Multilingual LLMs for China’s Minority Languages (2025.findings-acl)
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| Challenge: | Large language models excel in high-resource languages but struggle with low-resourced languages . minority languages such as Tibetan, Uyghur, Kazakh, and Mongolian are marginalized in NLP research due to limited digital representation and the scarcity of training data. |
| Approach: | They propose a benchmark for minority languages in China that tracks the progress of large language models on low-resource languages. |
| Outcome: | The proposed benchmark focuses on underrepresented writing systems and syntax-intensive tasks. |
IRIS: An Iterative and Integrated Framework for Verifiable Causal Discovery in the Absence of Tabular Data (2025.acl-long)
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| Challenge: | Existing statistical methods for causal discovery are expensive, require high-quality structured tabular data, and are often not available for a wide range of NLP applications. |
| Approach: | They propose a framework that combines statistical and large language model methods to discover causal relations from a set of initial variables. |
| Outcome: | The proposed method combines statistical and LLM-based methods to discover known and novel causal relations. |
FAA: Fine-grained Attention Alignment for Cascade Document Ranking (2023.acl-long)
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| Challenge: | Contemporary document ranking methods focus on transforming documents into passages to handle long inputs, but intensive query-irrelevant content may lead to harmful distraction and high query latency. |
| Approach: | They propose a fine-grained attention alignment approach to jointly optimize a cascade document ranking model. |
| Outcome: | Experiments on MS MARCO and TREC DL show that the proposed method is effective in document ranking tasks. |
Beyond Examples: Towards Automated Thought-level In-Context Reasoning for Large Language Models (2026.acl-long)
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Jinyang Wu, Mingkuan Feng, Shuai Zhang, Feihu Che, Zhengqi Wen, Chonghua Liao, Ling Yang, Haoran Luo, Zheng Lian, Jianhua Tao
| Challenge: | In-context learning (ICL) struggles with complex reasoning due to superficial, example-level implicit imitation. |
| Approach: | They propose an automated method that shifts from surface-level examples to more guidance-oriented thought patterns. |
| Outcome: | The proposed method achieves 80.6% accuracy on MATH and 62.5% on AMC, surpassing GPT-4o’s 77.2% and 57.5% accuracy. |
RENOVI: A Benchmark Towards Remediating Norm Violations in Socio-Cultural Conversations (2024.findings-naacl)
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Haolan Zhan, Zhuang Li, Xiaoxi Kang, Tao Feng, Yuncheng Hua, Lizhen Qu, Yi Ying, Mei Rianto Chandra, Kelly Rosalin, Jureynolds Jureynolds, Suraj Sharma, Shilin Qu, Linhao Luo, Ingrid Zukerman, Lay-Ki Soon, Zhaleh Semnani Azad, Reza Haf
| Challenge: | Norm violations occur when individuals fail to conform to culturally accepted behaviors, which may lead to potential conflicts. |
| Approach: | They propose to use a large corpus of 9,258 multi-turn dialogues annotated with social norms to equip AI systems with a remediation ability. |
| Outcome: | The proposed system can understand and remediate norm violations step by step. |
IMO: Greedy Layer-Wise Sparse Representation Learning for Out-of-Distribution Text Classification with Pre-trained Models (2024.acl-long)
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| Challenge: | IMO is a machine learning model that learns invariant features from unseen domains. |
| Approach: | They propose IMO: Invariant features Masks for Out-of-Distribution text classification to achieve OOD generalization by learning invariant feature masks. |
| Outcome: | The proposed model outperforms baseline models in various evaluation metrics and settings. |
Concept-Based Label Embedding via Dynamic Routing for Hierarchical Text Classification (2021.acl-long)
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| Challenge: | Existing methods for hierarchical text classification focus on modeling the text, but the concept of sharing among classes has been ignored in previous work. |
| Approach: | They propose a concept-based method that explicitly represents the concept and model the sharing mechanism among classes for the hierarchical text classification. |
| Outcome: | The proposed method outperforms state-of-the-art methods on two widely used datasets. |
SummerTime: Text Summarization Toolkit for Non-experts (2021.emnlp-demo)
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Ansong Ni, Zhangir Azerbayev, Mutethia Mutuma, Troy Feng, Yusen Zhang, Tao Yu, Ahmed Hassan Awadallah, Dragomir Radev
| Challenge: | Recent advances in summarization provide models that can generate high quality summaries . a new toolkit for summarizing text is being developed to make it easier for non-experts to keep track of them. |
| Approach: | They develop a toolkit for text summarization that integrates with libraries designed for NLP researchers. |
| Outcome: | SummerTime is a toolkit for text summarization, including models, datasets, and evaluation metrics. |
DPDV: Dual-Pathway and Dual-View Representation Learning for Bridging Information Asymmetry in Text-Video Retrieval (2026.acl-long)
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| Challenge: | Existing methods for text-based person anomaly search fail to address the pose-semantic gap . asymmetric cross-modal information poses a challenge to accurately establishing retrieval relationships . |
| Approach: | They propose a video retrieval framework that partitions visual features into two categories based on relevance to the text query and performs effective interaction. |
| Outcome: | The proposed framework achieves leading retrieval performance on five benchmark datasets. |
CEAN: Contrastive Event Aggregation Network with LLM-based Augmentation for Event Extraction (2024.eacl-long)
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| Challenge: | Event Extraction is a crucial yet arduous task in natural language processing (NLP), as its performance is hindered by laborious data annotation. |
| Approach: | They propose a Contrastive Event Aggregation Network with LLM-based Augmentation to promote low-resource learning and reduce data noise for event extraction. |
| Outcome: | The proposed approach achieves new state-of-the-art results on the ACE2005 and ERE-EN datasets. |
UniKeyphrase: A Unified Extraction and Generation Framework for Keyphrase Prediction (2021.findings-acl)
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| Challenge: | Mainstream methods that ignore the diversity among keyphrases or weakly capture the relation between tasks implicitly ignore keyphrase diversity. |
| Approach: | They propose a novel end-to-end learning framework that jointly learns to extract and generate keyphrases by exploiting latent semantic relation between extraction and generation. |
| Outcome: | The proposed approach outperforms mainstream methods on a benchmarked document on keyphrase prediction. |
ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning (2025.naacl-long)
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Vy Vo, Lizhen Qu, Tao Feng, Yuncheng Hua, Xiaoxi Kang, Songhai Fan, Tim Dwyer, Lay-Ki Soon, Gholamreza Haffari
| Challenge: | Existing methods for identifying event causality in NLP are limited in their scale and rely on lexical cues. |
| Approach: | They propose a benchmark for identifying abstract causality from a large-scale dataset. |
| Outcome: | The proposed benchmark can be leveraged for enhancing QA reasoning performance in LLMs. |
Pandora’s Box or Aladdin’s Lamp: A Comprehensive Analysis Revealing the Role of RAG Noise in Large Language Models (2025.acl-long)
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| Challenge: | Retrieval-Augmented Generation (RAG) has emerged as a promising approach to address hallucinations in large language models (LLMs). |
| Approach: | They define seven distinct noise types from a linguistic perspective and establish a Noise RAG Benchmark (NoiserBench) they propose to evaluate noise that is beneficial to LLMs and noise that's harmful to LRMs. |
| Outcome: | The proposed framework consists of seven distinct noise types from a linguistic perspective and includes multiple datasets and reasoning tasks. |
Causal Discovery Inspired Unsupervised Domain Adaptation for Emotion-Cause Pair Extraction (2024.findings-emnlp)
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Yuncheng Hua, Yujin Huang, Shuo Huang, Tao Feng, Lizhen Qu, Christopher Bain, Richard Bassed, Reza Haf
| Challenge: | Emotion-cause pair extraction is a task that aims to extract emotions and the events causing such emotions. |
| Approach: | They propose a deep latent model which captures the underlying latent structures of data and utilizes the easily transferable knowledge of emotions as the bridge to link the distributions of events in different domains. |
| Outcome: | The proposed model outperforms the strongest baseline by approximately 11.05% on a Chinese benchmark and 2.45% on an English benchmark in terms of weighted-average F1 score. |
SEA: Supervised Embedding Alignment for Token-Level Visual-Textual Integration in MLLMs (2025.emnlp-main)
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Yuanyang Yin, Yaqi Zhao, Yajie Zhang, Yuanxing Zhang, Ke Lin, Jiahao Wang, Xin Tao, Pengfei Wan, Wentao Zhang, Feng Zhao
| Challenge: | Multimodal Large Language Models (MLLMs) integrate visual and textual inputs, yet modality alignment remains one of the most challenging aspects. |
| Approach: | They propose a token-level supervision alignment method that enables more precise visual-text alignment during pretraining. |
| Outcome: | The proposed method improves performance across various model sizes, with smaller models benefiting the most. |
ZeroGen: Efficient Zero-shot Learning via Dataset Generation (2022.emnlp-main)
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| Challenge: | Existing approaches to generate training data with pre-trained language models have been found effective in various scenarios. |
| Approach: | They propose an unsupervised zero-shot learning method that generates a dataset from scratch and trains a tiny task model under supervision of the synthesized dataset. |
| Outcome: | The proposed method is annotated-free and efficient, but can provide useful insights from the perspective of data-free model-agnostic knowledge distillation and unreferenced text generation evaluation. |
Breaking Block Boundaries: Anchor-based History-stable Decoding for Diffusion Large Language Models (2026.acl-long)
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| Challenge: | Semi-autoregressive (Semi-AR) decoding suffers from inherent block constraints . naive lookahead decoding is unreliable, token stability closely correlates with convergence trend, and historical information is isolated. |
| Approach: | They propose a training-free, plug-and-play dynamic decoding strategy that monitors the stability of tokens in real time through dynamic anchors. |
| Outcome: | The proposed approach reduces decoding steps by 80% while improving performance by 3.67% on the BBH benchmark. |
Lost in the Context: Insufficient and Distracted Attention to Contexts in Preference Modeling (2025.acl-long)
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Shihan Dou, Jiayi Chen, Chenhao Huang, Feng Chen, Wei Chengzhi, Huiyuan Zheng, Shichun Liu, Yan Liu, Chenxiao Liu, Chao Xin, Lin Yan, Zongzhang Zhang, Tao Gui, Qi Zhang, Xuanjing Huang
| Challenge: | Existing reward models concatenate contexts and responses, but they often ignore crucial segments of the context that are important for evaluating the response quality. |
| Approach: | They propose a reward model that evaluates the response quality based on a given context and assigns a rewards reward. |
| Outcome: | The proposed framework significantly improves preference modeling by increasing attention to relevant information within the context and achieves better generalizability. |
Event Detection without Triggers (N19-1)
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| Challenge: | Existing approaches to event detection require annotated triggers and event types in training data. |
| Approach: | They propose a framework that encodes the representation of a sentence based on target event types. |
| Outcome: | The proposed framework achieves competitive performances compared with state-of-the-art methods. |
GLiM: Integrating Graph Transformer and LLM for Document-Level Biomedical Relation Extraction with Incomplete Labeling (2025.findings-acl)
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Hao Fang, Yuejie Zhang, Rui Feng, Yingwen Wang, Qing Wang, Wen He, Xiaobo Zhang, Tao Zhang, Shang Gao
| Challenge: | Document-level relation extraction (DocRE) solves problems of document quality . number of entities and entity-pair relations increases, causing incomplete annotations . |
| Approach: | a framework that reduces the problem space using a graph-enhanced Transformer-based model is proposed . GLiM leverages large language models for reasoning to reduce the problem-space . |
| Outcome: | GLiM boosts average recall and F1 scores on biomedical datasets . compared with existing models, GLim outperforms existing models on biomedicine benchmarks compared to existing models . |
Trust Within? Seek Beyond? Knowledge Boundary Aware Policy Optimization for Agentic Search (2026.acl-long)
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Tao Feng, Xinke Jiang, Xinyan Hu, Yonggang Zhang, Zhen Tao, Wentao Zhang, Boyang Liu, Wenhao Jiang, Chao Wu
| Challenge: | Existing approaches to augment large language models with external knowledge suffer from a lack of calibration regarding the model’s knowledge boundary. |
| Approach: | They propose a reinforcement learning framework that explicitly aligns retrieval decisions with quantified knowledge states. |
| Outcome: | The proposed framework outperforms strong baselines while exhibiting reduced hallucination rates. |
Mixed Distillation Helps Smaller Language Models Reason Better (2024.findings-emnlp)
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| Challenge: | Recent large language models (LLMs) have demonstrated impressive multiple step-by-step reasoning capabilities in recent NLP reasoning tasks. |
| Approach: | They propose a mixed distillation framework that distills multiple step-by-step reasoning abilities into smaller language models (SLMs) they leverage LLMs to generate multiple step by step reasoning rationales by sampling automatically. |
| Outcome: | The proposed framework outperforms existing models on SVAMP, GSM8K and ASDIV, while a single model generated by MD exceeds the comprehensive performance of two individual CoT and PoT distilled models. |
Rectifying the Emotional Flow: Aligning Priors and Dynamic Guidance for High-Arousal Text-to-Speech (2026.acl-long)
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| Challenge: | Existing systems suffer from linguistic collapse when pursuing high intensity or fail to meet target emotional levels. |
| Approach: | They propose an inference framework that introduces a neutral prosody bias and a uniform Classifier-Free Guidance that distorts the acoustic manifold, leading to artifacts. |
| Outcome: | The proposed framework achieves superior linguistic accuracy and expressiveness without model retraining. |
Let’s Negotiate! A Survey of Negotiation Dialogue Systems (2024.findings-eacl)
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Haolan Zhan, Yufei Wang, Zhuang Li, Tao Feng, Yuncheng Hua, Suraj Sharma, Lizhen Qu, Zhaleh Semnani Azad, Ingrid Zukerman, Reza Haf
| Challenge: | Recent research has focused on negotiation dialogue systems, but no systematic review of this task has been conducted. |
| Approach: | They propose to provide a systematic review of negotiation dialogue systems and to provide an overview of current research. |
| Outcome: | The proposed systems are based on the literature and are compared against existing systems. |
Sparse Frame Grouping Network with Action Centered for Untrimmed Video Paragraph Captioning (2023.findings-emnlp)
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| Challenge: | Existing methods for paragraph captioning videos without event ground truths generate one sentence for each event, but without event labels, it is difficult to locate the transitions between events and minimize repetition. |
| Approach: | They propose a module that dynamically groups event information with the help of action information for the entire video and excludes redundant frames within pre-defined clips. |
| Outcome: | The proposed module outperforms the state-of-the-art methods on all metrics. |
Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs (2025.acl-long)
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| Challenge: | Existing methods for retrieving historical LLM responses are lacking in long-context summarization tasks. |
| Approach: | They propose a graph of records which leverages historical LLM responses to enhance RAG for long-context global summarization. |
| Outcome: | The proposed method improves on four long-context summarization datasets. |
Attend, Select and Eliminate: Accelerating Multi-turn Response Selection with Dual-attention-based Content Elimination (2023.findings-acl)
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| Challenge: | Pre-trained language models can be used to perform multi-turn response selection, but they can be expensive. |
| Approach: | They propose a framework and a strategy that progressively selects and eliminates unimportant content under context-response dual-attention. |
| Outcome: | The proposed method can effectively speed-up SOTA models without much performance degradation and shows a better trade-off between speed and performance than previous methods. |
Less is More: Mitigate Spurious Correlations for Open-Domain Dialogue Response Generation Models by Causal Discovery (2023.tacl-1)
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| Challenge: | Existing models suffer from spurious correlations and generate irrelevant and generic responses. |
| Approach: | They propose a model-agnostic method for training and inference using a conditional independence classifier that overcomes data sparsity. |
| Outcome: | The proposed method outperforms the baseline models in relevance, informativeness, and fluency. |
Scaling Law for Multimodal Large Language Model Supervised Fine-Tuning (2026.acl-long)
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YiFan Zhang, Tao Yu, Feng Li, Chaoyou Fu, Yibo Hu, Kun Wang, Qingsong Wen, Zhang Zhang, Liang Wang, Rong Jin
| Challenge: | supervised fine-tuning (SFT) is crucial for multimodal large language models, yet a comprehensive scaling law is lacking . et al.: scaling laws focus on model size, pre-training tokens, and MLLM SFT data volumes . |
| Approach: | They propose two scaling laws to guide optimal model-data configuration . they propose one applicable when training data volumes are well defined by researchers . |
| Outcome: | The proposed scaling laws provide valuable recommendations for optimal resource allocation . they show that the proposed laws are more accurate than existing models . |
ProGen: Progressive Zero-shot Dataset Generation via In-context Feedback (2022.findings-emnlp)
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| Challenge: | Recent work on dataset-generation-based zero-shot learning has shown promising results by training a task-specific model with a dataset synthesized from large pre-trained language models (PLMs). |
| Approach: | They propose a progressive zero-shot dataset generation framework which leverages feedback from the task-specific model to guide the generation of new training data via in-context examples. |
| Outcome: | The proposed framework achieves on-par or superior performance with only 1% synthetic dataset size, when compared to baseline methods without in-context feedback. |
MathFlow: Enhancing the Perceptual Flow of MLLMs for Visual Mathematical Problems (2026.acl-long)
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| Challenge: | Existing multimodal large language models (MLLMs) exhibit significant limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs. |
| Approach: | They propose a benchmark that provides a fine-grained evaluation of MLLMs’ perception and reasoning capabilities. |
| Outcome: | The proposed benchmark shows that existing MLLMs exhibit limitations when extracting essential information and reasoned properties from diagrams and performing complex reasoning based on these visual inputs. |